Stat 406

Methods for Statistical Learning

At the end of the course, you will be able to:

  • Assess the prediction properties of the supervised learning methods covered in class;
  • Correctly use regularization to improve predictions from linear models, and also to identify important explanatory variables;
  • Explain the practical difference between predictions obtained with parametric and non-parametric methods, and decide in specific applications which approach should be used;
  • Select and construct appropriate ensembles to obtain improved predictions in different contexts;
  • Use and interpret principal components and other dimension reduction techniques;
  • Employ reasonable coding practices and understand basic R syntax and function.
  • Write reports and use proper version control; engage with standard software.